



'''simple docstring'''



import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def        UpperCAmelCase (    *A	:					List[str]					):
							if not isinstance(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      list(A					)
							for i in range(len(A					)					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
							gc.collect()
							if is_xpu_available():
														torch.xpu.empty_cache()
							elif is_npu_available():
														torch.npu.empty_cache()
							else:
														torch.cuda.empty_cache()
							return objects
def        UpperCAmelCase (    A	:					Exception					):
							SCREAMING_SNAKE_CASE       :   Dict         =      [
							    '''CUDA out of memory.''',  # CUDA OOM
							    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
							    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
							]
							if isinstance(A       ,		A					) and len(exception.args					) == 1:
														return any(err in exception.args[0] for err in _statements					)
							return False






def        UpperCAmelCase (    A	:					callable = None       ,		A	:					int = 128					):
							if function is None:
														return functools.partial(A       ,		starting_batch_size=A					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      starting_batch_size

							def decorator(*A	:					Tuple       ,		**A	:					List[str]					):
														nonlocal batch_size
														gc.collect()
														if is_xpu_available():
																					torch.xpu.empty_cache()
														elif is_npu_available():
																					torch.npu.empty_cache()
														else:
																					torch.cuda.empty_cache()
														SCREAMING_SNAKE_CASE       :   List[Any]         =      list(inspect.signature(A					).parameters.keys()					)
														# Guard against user error
														if len(A					) < (len(A					) + 1):
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      ''', '''.join([F"""{arg}={value}""" for arg, value in zip(params[1:]       ,		args[1:]					)]					)
																					raise TypeError(
																					    F"""Batch size was passed into `{function.__name__}` as the first argument when called."""
																					    F"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`"""					)
														while True:
																					if batch_size == 0:
																												raise RuntimeError('''No executable batch size found, reached zero.'''					)
																					try:
																												return function(A       ,		*A       ,		**A					)
																					except Exception as e:
																												if should_reduce_batch_size(A					):
																																			gc.collect()
																																			if is_xpu_available():
																																										torch.xpu.empty_cache()
																																			elif is_npu_available():
																																										torch.npu.empty_cache()
																																			else:
																																										torch.cuda.empty_cache()
																																			batch_size //= 2
																												else:
																																			raise

							return decorator









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase_  :							Dict             =    {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_  :							List[Any]             =    [
							    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'EncodecModel',
							    'EncodecPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_encodec import (
							    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EncodecConfig,
							)
							from .feature_extraction_encodec import EncodecFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encodec import (
														    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EncodecModel,
														    EncodecPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_  :							List[str]             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




